Academic Nurse-Managed Community Clinics Transitioning to Telehealth: Case Report on the Rapid Response to COVID-19
Bibliographic record
Abstract
BACKGROUND: In response to the COVID-19 pandemic, many health care organizations have adopted telehealth. The current literature on transitioning to telehealth has mostly been from large health care or specialty care organizations, with limited data from safety net or community clinics. OBJECTIVE: This is a case report on the rapid implementation of a telehealth hub at an academic nurse-managed community clinic in response to the national COVID-19 emergency. We also identify factors of success and challenges associated with the transition to telehealth. METHODS: This study was conducted at the George Mason University Mason and Partners clinic, which serves the dual mission of caring for community clinic patients and providing health professional education. We interviewed the leadership team of Mason and Partners clinics and summarized our findings. RESULTS: Mason and Partners clinics reacted quickly to the COVID-19 crisis and transitioned to telehealth within 2 weeks of the statewide lockdown. Protocols were developed for a coordination hub, a main patient triage and appointment telephone line, a step-by-step flowchart of clinical procedure, and a team structure with clearly defined work roles and backups. The clinics were able to maintain most of its clinical service and health education functions while adapting to new clinic duties that arose during the pandemic. CONCLUSIONS: The experiences learned from the Mason and Partners clinics are transferable to other safety net clinics and academic nurse-led community clinics. The changes arising from the pandemic have resulted in sustainable procedures, and these changes will have a long-term impact on health care delivery and training.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".